





'''simple docstring'''

import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__lowerCAmelCase				:      Union[str, Any]						      ="""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase				:      List[str]						      =direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase				:      Union[str, Any]						      =re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__lowerCAmelCase				:      Optional[int]						      =re.compile(R"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowerCAmelCase				:      Optional[Any]						      =re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCAmelCase				:      int						      =[
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]





def 		UpperCamelCase							(				_lowerCamelCase   :   Tuple			):
    A__							=     re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,			_lowerCamelCase			)
    return [m.group(0			) for m in matches]





def 		UpperCamelCase							(				):
    A__							=     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    A__							=     {
        config.replace("Config"		,			""			): model_type for model_type, config in config_maping_names.items()
    }

    # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
    A__							=     collections.defaultdict(_lowerCamelCase			)
    A__							=     collections.defaultdict(_lowerCamelCase			)
    A__							=     collections.defaultdict(_lowerCamelCase			)

    # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
    for attr_name in dir(_lowerCamelCase			):
        A__							=     None
        if _re_tf_models.match(_lowerCamelCase			) is not None:
            A__							=     tf_models
            A__							=     _re_tf_models.match(_lowerCamelCase			).groups()[0]
        elif _re_flax_models.match(_lowerCamelCase			) is not None:
            A__							=     flax_models
            A__							=     _re_flax_models.match(_lowerCamelCase			).groups()[0]
        elif _re_pt_models.match(_lowerCamelCase			) is not None:
            A__							=     pt_models
            A__							=     _re_pt_models.match(_lowerCamelCase			).groups()[0]

        if lookup_dict is not None:
            while len(_lowerCamelCase			) > 0:
                if attr_name in model_prefix_to_model_type:
                    A__							=     True
                    break
                # Try again after removing the last word in the name
                A__							=     "".join(camel_case_split(_lowerCamelCase			)[:-1]			)

    A__							=     set(list(pt_models.keys()			) + list(tf_models.keys()			) + list(flax_models.keys()			)			)
    A__							=     list(_lowerCamelCase			)
    all_models.sort()

    A__							=     {"model_type": all_models}
    A__							=     [pt_models[t] for t in all_models]
    A__							=     [tf_models[t] for t in all_models]
    A__							=     [flax_models[t] for t in all_models]

    # Now let's use the auto-mapping names to make sure
    A__							=     {}
    for t in all_models:
        if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
            A__							=     "AutoProcessor"
        elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
            A__							=     "AutoTokenizer"
        elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
            A__							=     "AutoFeatureExtractor"
        else:
            # Default to AutoTokenizer if a model has nothing, for backward compatibility.
            A__							=     "AutoTokenizer"

    A__							=     [processors[t] for t in all_models]

    return pd.DataFrame(_lowerCamelCase			)





def 		UpperCamelCase							(				_lowerCamelCase   :   List[str]			):
    A__							=     [
        transformers_module.models.auto.modeling_auto,
        transformers_module.models.auto.modeling_tf_auto,
        transformers_module.models.auto.modeling_flax_auto,
    ]
    for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
        A__							=     [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
        A__							=     [auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
        # Loop through all three frameworks
        for module, cls, mapping in zip(_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			):
            # The type of pipeline may not exist in this framework
            if not hasattr(_lowerCamelCase		,			_lowerCamelCase			):
                continue
            # First extract all model_names
            A__							=     []
            for name in getattr(_lowerCamelCase		,			_lowerCamelCase			).values():
                if isinstance(_lowerCamelCase		,			_lowerCamelCase			):
                    model_names.append(_lowerCamelCase			)
                else:
                    model_names.extend(list(_lowerCamelCase			)			)

            # Add pipeline tag and auto model class for those models
            table.update({model_name: (pipeline_tag, cls) for model_name in model_names}			)

    return table





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   Optional[int]			):
    A__							=     get_frameworks_table()
    A__							=     Dataset.from_pandas(_lowerCamelCase			)

    A__							=     hf_hub_download(
        "huggingface/transformers-metadata"		,			"pipeline_tags.json"		,			repo_type="dataset"		,			token=_lowerCamelCase			)
    A__							=     Dataset.from_json(_lowerCamelCase			)
    A__							=     {
        tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
        for i in range(len(_lowerCamelCase			)			)
    }
    A__							=     update_pipeline_and_auto_class_table(_lowerCamelCase			)

    # Sort the model classes to avoid some nondeterministic updates to create false update commits.
    A__							=     sorted(table.keys()			)
    A__							=     pd.DataFrame(
        {
            "model_class": model_classes,
            "pipeline_tag": [table[m][0] for m in model_classes],
            "auto_class": [table[m][1] for m in model_classes],
        }			)
    A__							=     Dataset.from_pandas(_lowerCamelCase			)

    with tempfile.TemporaryDirectory() as tmp_dir:
        frameworks_dataset.to_json(os.path.join(_lowerCamelCase		,			"frameworks.json"			)			)
        tags_dataset.to_json(os.path.join(_lowerCamelCase		,			"pipeline_tags.json"			)			)

        if commit_sha is not None:
            A__							=     (
                F"Update with commit {commit_sha}\n\nSee: "
                F"https://github.com/huggingface/transformers/commit/{commit_sha}"
            )
        else:
            A__							=     "Update"

        upload_folder(
            repo_id="huggingface/transformers-metadata"		,			folder_path=_lowerCamelCase		,			repo_type="dataset"		,			token=_lowerCamelCase		,			commit_message=_lowerCamelCase		,			)





def 		UpperCamelCase							(				):
    A__							=     {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
    A__							=     transformers_module.pipelines.SUPPORTED_TASKS
    A__							=     []
    for key in pipeline_tasks:
        if key not in in_table:
            A__							=     pipeline_tasks[key]["pt"]
            if isinstance(_lowerCamelCase		,			(list, tuple)			):
                A__							=     model[0]
            A__							=     model.__name__
            if model not in in_table.values():
                missing.append(_lowerCamelCase			)

    if len(_lowerCamelCase			) > 0:
        A__							=     ", ".join(_lowerCamelCase			)
        raise ValueError(
            "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
            F"`utils/update_metadata.py`: {msg}. Please add them!"			)


if __name__ == "__main__":
  __lowerCAmelCase				:      List[str]						      =argparse.ArgumentParser()
  parser.add_argument("--token", type=str, help="The token to use to push to the transformers-metadata dataset.")
  parser.add_argument("--commit_sha", type=str, help="The sha of the commit going with this update.")
  parser.add_argument("--check-only", action="store_true", help="Activate to just check all pipelines are present.")
  __lowerCAmelCase				:      Any						      =parser.parse_args()

  if args.check_only:
    check_pipeline_tags()
  else:
    update_metadata(args.token, args.commit_sha)




"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			["""ReformerTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[str]									=			["""ReformerTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:int									=			[
       """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ReformerAttention""",
       """ReformerForMaskedLM""",
       """ReformerForQuestionAnswering""",
       """ReformerForSequenceClassification""",
       """ReformerLayer""",
       """ReformerModel""",
       """ReformerModelWithLMHead""",
       """ReformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE__:int									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
